SOURCE_DIGEST: af49fec1e4600b281b34195c23a49334eceba91ac6fdb4f157cf2e40524acc95

The manuscript delivers a rigorously disciplined, constraint-aware specification for the CatDB identity and reconciliation layer (P7). It successfully defines a falsifiable hypothesis and establishes a strict evidence boundary, deferring implementation claims until runtime and formal artifacts exist.

### Semantic Audit
- **Source vs. Canonical Identity:** The separation is structurally enforced. Section 4 correctly isolates immutable `SourceRef` from profile-relative `CanonicalRef`.
- **States:** The paper defines a complete state machine (candidate, possible, probable, confirmed-same, confirmed-different, contradicted, superseded, retracted, expired) in Table 2, expressly refusing transitive closure for uncertain states.
- **Uncertain-to-Confirmed Promotion:** Requirement 5.1 strictly forbids promoting candidates or probabilities to equivalence without authorized transitions.
- **Calibration vs. Scores:** Section 6.3 properly distinguishes uncalibrated classifier outputs from calibrated posteriors, mandating proper scoring rules and reliability diagrams.
- **Vector/ANN Results:** Requirement 6.1 successfully enforces the vector safety boundary, restricting embeddings and HNSW/Faiss retrieval strictly to candidate generation.
- **Clustering vs. Pairs and Constraints:** Section 6.5 delineates pair scores from partitions, and Section 5.3 strictly honors `cannot-link` constraints by yielding conflict witnesses rather than silently dropping them.
- **Copied-source & Dependencies:** Sections 6.5 and 16.2 acknowledge the risks of dependent evidence and collective inference double-counting.
- **Authority, Ownership, and Truth:** Section 4.5 perfectly disaggregates observation strength, statistical confidence, source reliability, source ownership, reviewer authority, and modeled truth. 
- **Scoped Human Approval:** Sections 6.6 and 7.1 properly frame human review as an auditable, scoped, fallible, and reversible event rather than an infallible oracle.
- **Identity vs. Attribute Fusion:** Section 7.2 correctly states that merging records does not automatically resolve attribute conflicts.
- **Supersession:** Section 8.2 requires typed reasons for supersession, recognizing that replacement does not uniformly imply sameness.
- **Logical Reversal:** Section 8.3 explicitly bounds logical reversal to an admitted, provenance-complete fragment, noting that physical repair and external compensation remain necessary.
- **Integration Contracts:** Dependencies on P4 (mappings), P5 (federation), P6 (materialization), P8 (writes), P9 (workspaces), and P13 (provenance) are explicitly declared and their interaction constraints defined (Sections 2.4, 9).
- **OWL, SKOS, PROV, and Privacy:** OWL's strong equality is correctly distinguished from SKOS mapping relations. PROV-O is adopted for interchange (Table 3), and privacy (e.g., Bloom filters) claims are appropriately scoped to prior art.

### Novelty and Prior Art
Section 12 correctly attributes statistical foundations to Fellegi-Sunter, duplicate detection to Jaro and Hernandez-Stolfo, collective resolution to Bhattacharya-Getoor, and acknowledges standard blocking, active learning, and human-in-the-loop systems. The paper correctly restricts its novelty claim to the composition of identity artifacts with CatDB's typed mappings, provenance, bitemporal history, and materialization boundaries. 

### Evidence Boundary and Falsifiability
The manuscript demonstrates impeccable discipline regarding its evidence boundary. Sections 2.3 and 15 explicitly state the absence of a runtime, fixtures, Rust/Haskell implementations, Lean theorems, vector adapters, federation integration, security/privacy results, and evaluation benchmarks. 
Appendix A contains 33 exhaustive nonclaims, explicitly rejecting that similarity establishes identity, that category theory discovers real-world identity, that accepted schema/mapping work acts as identity evidence, or that provenance proves truth. The falsification conditions in Section 3.3 and the promotion gates in Section 14.7 are clear, measurable, and rigorous. 

### Running Example and Conjectures
The running example in Section 11 accurately applies the constraints, illustrating how vector results remain candidates, uncalibrated scores remain classifier-scores, and contradictory constraints halt automated merges. Conjectures (Sections 10.3–10.6) are explicitly labeled as open, correctly reflecting the absence of formal proofs. 

### Conclusion
There are no unsupported novelty claims, technical errors, missing qualifications, citation defects, contradictions, or non-reproducible claims. The paper perfectly executes its intended role as a specification and evidence contract. All future tasks are explicitly categorized as unimplemented engineering and experimental milestones rather than presented as current capabilities.

VERDICT: ACCEPT